Welcome to Biology 395, Advanced Bioassessment. In this course we’ll:

• Review the basis of Bioassessment (Think conservation biology and genetics)
• Review Run off sources
• Review Water Quality monitoring its uses, and how to gather these data.
• Review Biological Monitoring methods.
• Review the basis of Indices of Biological Integrity
• Gather and analyze VSCI data from a local Appomattox tributary.
• eDNA now and in the future.

Mainly, you’ll be taking a look at and reviewing my lectures outside of class in Canvas. Once you review those, you’ll come to class and we’ll practice what you’ve looked at in the lectures prior to class. There will be two exams, one lab report, and lots of small assignments based in R and that will total up your grade.

General outcomes:

Specific outcomes:

• Be able to import and manipulate data in R
• Assess a stream for physical habitat
• Assess a stream for water quality
• Perform Biological Monitoring
• Analyze these data in R
• Discuss the use case(s) of eDNA

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggpubr) 
library("FactoMineR")
library("factoextra")

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

Today, we’re going to take a look at a brief overview of what’s in the course starting with conservation biology/genetics.

knitr::include_graphics("Conservationgenetics_model.jpeg")

# ![](/Users/jamesonhinkle/Desktop/folders/Longwood/Bio395/Conservationgenetics_model.jpeg) 

We’ll look into the details of run off sources and how they’re monitored, mainly in Virginia via the USGS and DEQ.

knitr::include_graphics("Runoff_sources.png")

We’ll also talk in a lot of depth about biological monitoring. This is really at the center of the course as this completely relies on our ability to generate indices of biological integrity.

Speaking of Indices of Biological Integrity (IBI), we’ll talk in depth about how these are generated, and how they’re applied. Indices of biological integrity have been around now for decades. These include but are not limited to:

• Shannon Diversity Index
• Simpson Diversity Index

The links above are really just references for you. We’ll talk in more detail about these later.

As indices developed, multimetric indices of biological integrity were developed and requested by the EPA so that states could have their own metrics and define what is impaired streams under the 303(d) section of the Clean Water Act.

• Virginia developed it’s own IBI known as the Virginia Stream Condition Index VSCI that we’ll discuss in depth.

• A need was also identified for a multimetric index for streams that are not of a “higher gradient.” Research to develop this index through the mid-2000s produced the Coastal Plain Macroinvertebrate Index CPMI

We will talk about both of these in detail and learn how to analyze basic VSCI and CPMI datasets in R.

eDNA or environmental DNA is a tool used in lieu of doing physical biologial assessment. It can be used to detect cryptic, invasive, or endangered species without harming them with any sort capture method. The method isolates DNA from a number of “matrices” namely: - water - soil - bone/excrement

knitr::include_graphics("edna_image.jpg")

Now, eDNA includes things a broad as metagenomics where we isolate DNA from many species in one substrate (mostly commonly, water) to look at what’s present in entire ecological communities.

Good review examples:

• Willerslev et al 2015

• Willserlev et al 2015 (again)

• Lodge et al 2012

• R itself consists of an underlying engine that takes commands and provides feedback on these commands. Each command you give the R engine is either an:

• Expression An expression is a statement that you give the R engine. R will evaluate the expression, give you the answer and not keep any reference to it for future use. Some examples include:

2 + 6

## [1] 8

sqrt(5)

## [1] 2.236068

3 * (pi/2) - 1

## [1] 3.712389

-Assignment

An assignment causes R to evaluate the expression and stores the result in a variable. This is important because you can use the variable in the future. An example of an assignment is:

x <- 2 + 6
myCoolVariable <- sqrt(5)
another_one_number23 <- 3 * (pi/2) - 1
x

## [1] 8

myCoolVariable

## [1] 2.236068

another_one_number23

## [1] 3.712389

Functions may have more than one parameter passed to it. Often if there are a lot of parameters given then there will be some default values provided. For example, the log() function provides logarithms. The definition of the log function show log(x, base=exp(1)) (say from ?log). Playing around with the function shows:

log(2)

## [1] 0.6931472

log(2, base = 2)

## [1] 1

log(2, base = 10)

## [1] 0.30103

• Numeric

Numeric types represent the majority of numerical valued items you will deal with. When you assign a number to a variable in R it will most likely be a numeric type. Numeric data types can either be displayed with or without decimal places depending if the value(s) include a decimal portion. In fact, R will make any assignment of a numerical value a numeric by default. For example:

x <- 4
class(x)

## [1] "numeric"

x

## [1] 4

x <- numeric(4)
x

## [1] 0 0 0 0

x[1] = 2.4
x

## [1] 2.4 0.0 0.0 0.0

Notice this is an all or nothing deal here, each element of a vector must be the same type and the de- fault value for a numeric data types is zero. Also notice (especially those who have some experience in programming other languages) that dimensions in vectors (and matrices) start at 1 rather than 0. Operations on numeric types proceed as you would expect but since the numeric type is the default type, you don’t really have to go around using the as.numeric(x) function. For example:

is.numeric(2.4)

## [1] TRUE

as.numeric(2) + 0.4

## [1] 2.4

2 + 0.4

## [1] 2.4

• Numeric

Word of Caution, It is important to point out here that you need to be rather careful when dealing with floating point numbers due in part to the way in which computers store these numbers and how they are presented to us in the R interface as well as when we need to perform logical operations on them. Consider the following case. The ancient Egyptians had an approach to calculating π as the ratio of 256/81.

e.pi <- 256/81
e.pi

## [1] 3.160494

• Numeric

Word of Caution cont’d: Very nice and apparently pretty close to 3.1416 so that they could get work done. Now, as we all know, the value of π is the ratio of a circle’s circumference to its diameter. We also know that it is a transcendeental number (e.g., on that cannot be produced using finite algebraic operations) and its decimal values never repeat.

print(e.pi, digits = 20)

## [1] 3.1604938271604936517

• Numeric

Word of Caution cont’d: There is another issue that you need to be careful with. You need to be considerate of how a computer stores numerical values. Consider the following:

x <- 0.3/3
x

## [1] 0.1

print(x, digits = 20)

## [1] 0.099999999999999991673

• Character

The character data type is the one that handles letters and letter-like representations of numbers. For example, observe the following:

x <- "If you can read this, you are beginning to take a step into a larger world."
class(x)

## [1] "character"

length(x)

## [1] 1

• Character

Notice here how the variable x has a length of one, even though there are 37 characters within that string. If you want to know the number of characters, you need to use the nchar() function, otherwise it will tell you the ’vector length’ (see below) of the variable.

y <- 23
class(y)

## [1] "numeric"

z <- as.character(y)
z

## [1] "23"

class(z)

## [1] "character"

w = "cannot"
x = "I"
y = "can"
z = "code in R"

paste(x, w, z)

## [1] "I cannot code in R"

paste(x, y, z)

## [1] "I can code in R"